This function draws Blacklights on a canvas using a Support Vector Machine (SVM) algorithm. SVM's are a type of supervised learning algorithm that can be used for classification and regression purposes. The main goal of the SVM technique is to find a hyperplane (decision boundary) that best separates the values in the training dataset. This function draws the predictions from the SVM algorithm fitted on a randomly generated continuous training data set.
canvas_blacklight(
colors,
n = 1000,
resolution = 500
)Arguments
- colors
a string or character vector specifying the color(s) used for the artwork.
- n
a positive integer specifying the number of random data points to generate.
- resolution
resolution of the artwork in pixels per row/column. Increasing the resolution increases the quality of the artwork but also increases the computation time exponentially.
Value
A ggplot object containing the artwork.
See also
colorPalette
Examples
# \donttest{
set.seed(1)
# Simple example
canvas_blacklight(colors = colorPalette("tuscany2"))
# }